Diesel eission fluid quality detection system and method

ABSTRACT

An exhaust treatment system is provided including: a selective catalyst reduction (SCR) unit; a reducing agent dispensing unit configured to introduce a reducing agent into the exhaust; a first NO X  sensor upstream of the SCR unit; a second NO X  sensor at a location downstream of the SCR unit; a first temperature sensor at a location upstream of where the reducing agent is introduced into the exhaust; a second temperature sensor at a location downstream of where the reducing agent is introduced into the exhaust and upstream of the SCR unit; and a controller configured to determine a reductant quality indicator according to a NO X  differential between the first NO X  sensor and the second NO X  sensor relative to a predicted NO X  differential and a temperature differential between the first temperature sensor and the second temperature sensor relative to a predicted temperature differential.

TECHNICAL FIELD

Embodiments of the present disclosure pertain to a diesel emission fluidquality detection system and method.

BACKGROUND

Increasingly stringent government standards associated with combustionengine emissions have increased the burden on manufacturers to reducethe amount of nitrogen oxides (NO_(X)) and particulates that may beenitted from their developed engines. Along with this burden is themanufacturer's commitment to its customers to produce fuel efficientengines.

One known type of NO_(X) reduction technique is selective catalyticreduction (SCR). This technique of reducing NO_(X) in a combustionengine generally includes the use of reductants, such as ammonia,aqueous urea, and other compounds, in conjunction with an appropriatecatalyst material.

In a conventional open loop control urea based SCR system, a urea pumpmay provide a pressurized supply of urea to an atomizer or injector,which then injects the a urea solution into the exhaust stream of acombustion engine. An SCR controller may control the rate of urea thatis being applied to the atomizer. Within the exhaust stream, the ureasolution may decompose into ammonia (NH₃) and water vapor above certaintemperatures, such as 160 degrees C. When the exhaust gas mixture ispassed over an SCR catalyst, the NO_(X) and NH₃ molecules react with thecatalyst and generally produce diatomic nitrogen (N₂) and water (H₂O.

The ability of an SCR catalyst to reduce NO_(X) depends upon manyfactors, such as catalyst formulation, the size of the catalyst, exhaustgas temperature, and urea dosing rate. With regard to the dosing rate,the NO_(X) reduction efficiency tends to increase linearly until thedosing rate reaches a certain limit. Above the limit, the efficiency ofthe NO_(X) reduction may start to increase at a slower rate. One reasonfor the decline in the NO_(X) reduction efficiency is than the ammoniamay be supplied at a faster rate than the NO_(X) reduction process canconsume. The excess ammonia, known as ammonia slip, may be expelled fromthe SCR catalyst.

In order for an optimal NO_(X) reduction to take place, the integrity ofthe reductant (e.g., urea) must be maintained. For instance, if thereductant is diluted (e.g., in water) or overly concentrated, an idealreaction in the SCR system will not occur. Thus, to promote an optimalreaction, it is beneficial to ensure the quality of the reductant.

Physical sensors are widely used in many products to measure and monitorphysical phenomena, such as temperature, speed, and emissions from motorvehicles. Physical sensors often take direct measurements of thephysical phenomena and convert these measurements into measurement datato be further processed by control systems. For example U.S. Pat. No.7,216,478 describes a method of monitoring a dosing system.

Although physical sensors take direct measurements of the physicalphenomena, physical sensors and their associated hardware are oftencostly and, sometimes, unreliable. For instance, directly measuring thequality of a reductant, such as urea, with physical sensors in a fieldenvironment is difficult and may be unreliable.

Instead of direct measurements, virtual sensors have been developed toprocess other various physically measured values and to produce valuesthat were previously measured directly by physical sensors. For example,U.S. Pat. No. 5,386,373 (the '373 patent) issued to Keeler et al. onJan. 31, 1995, discloses a virtual continuous emission monitoring systemwith sensor validation. The '373 patent uses a backpropagation-to-activation model and a Monte Carlo search technique toestablish and optimize a computational model used for the virtualsensing system to derive sensing parameters from other measuredparameters.

SUMMARY

According to aspects disclosed herein, a system and method are providedto detect the quality of a reductant according to sensor differentials.

According to an aspect of an embodiment herein, an exhaust treatmentsystem for treating a flow of exhaust produced by an engine isdisclosed. The exhaust treatment system for treating a flow of exhaustproduced by an engine includes: a selective catalyst reduction (SCR)unit; a reducing agent dispensing unit configured to introduce areducing agent into the exhaust; a first NO_(X) sensor configured toindicate a NO_(X) emission level of the exhaust at a location upstreamof the SCR unit; a second NO_(X) sensor configured to indicate a NO_(X)emission level of the exhaust at a location downstream of the SCR unit;a first temperature sensor configured to indicate a temperature of theexhaust at a location upstream of where the reducing agent is introducedinto the exhaust; a second temperature sensor configured to indicate atemperature of the exhaust at a location downstream of where thereducing agent is introduced into the exhaust and upstream of the SCRunit; and a controller configured to electronically communicate with thefirst NO_(X) sensor, the second NO_(X) sensor, the first temperaturesensor, and the second temperature sensor, and to determine a reductantquality indicator according to a NO_(X) differential between the firstNO_(X) sensor and the second NO_(X) sensor relative to a predictedNO_(X) differential and a temperature differential between the firsttemperature sensor and the second temperature sensor relative to apredicted temperature differential.

According to an aspect of an embodiment herein, method for detecting areducing agent quality is disclosed. The method for detecting a reducingagent quality includes: obtaining a first NO_(X) value indicating aNO_(X) level for an engine exhaust upstream of a selective catalystreduction (SCR) unit; obtaining a second NO_(X) value indicating aNO_(X) emission level for the engine exhaust downstream of the SCR unit;obtaining a first temperature value indicating a temperature for theengine exhaust upstream of an introduction of a reducing agent;obtaining a second temperature value indicating a temperature for theengine exhaust downstream of the introduction of the reducing agent andupstream of the SCR unit; and computing a reductant quality indicatoraccording to a NO_(X) differential between the first NO_(X) value andthe second NO_(X) value relative to a predicted NO_(X) differential anda temperature differential between the first temperature value and thesecond temperature value relative to a predicted temperaturedifferential.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary machine according to a embodimentdescribed herein;

FIG. 2 is a block diagram of a reductant quality detection system in anafter-treatment system according to an embodiment herein;

FIG. 3 is a block diagram of a method of detecting reductant qualityaccording to an embodiment herein.

DETAILED DESCRIPTION

Exemplary embodiments of the present invention are presented herein withreference to the accompanying drawings. Herein, like numerals designatelike parts throughout.

FIG. 1 illustrates an exemplary machine 100 according to a embodimentdescribed herein. The machine 100 may refer to any type of stationary ormobile machine that performs some type of operation associated with aparticular industry. The machine 100 may also include any type ofcommercial vehicle, such as cars, trucks, vans, boats, ships, and othervehicles or machines, such as power generators and stationary gascompressors.

FIG. 2 is a block diagram of a reductant quality detection system in anafter-treatment system according to an embodiment herein. According toFIGS. 1 and 2, a machine 100 may include an exhaust treatment system200. The exhaust treatment system 200 may include: an engine 102, aselective catalytic reduction (SCR) unit 108, a reductant system 106(e.g., a urea reservoir/tank, a pump, and injection components), andsensor network 104.

The engine 102 generates an exhaust stream that is transmitted to theSCR unit 108. Before reaching the SCR the exhaust may be optionallyrouted through one or more after treatment elements, e.g., a dieselparticulate filter (DPF) configured to reduce the amount of particulatesin the exhaust. By passing the exhaust through a DPF, prior to the SCRunit 108, particulates in the exhaust may be removed. Removingparticulates from exhaust prior to use of a physical NO_(X) sensor mayincrease the operational life of the sensor.

The reductant system 106 is for holding and injecting a reductant, suchas urea, ammonia, or any other reductant according to the specific SCRsystem.

According to an embodiment herein, the reductant system 106 isconfigured to supply a urea reductant to the SCR unit 108 for reducingthe exhaust NO_(X). For instance, the urea from the reductant system 106may be combined with the exhaust from the engine 102 upstream of the SCRunit 108 in order to mix with the exhaust prior to entering the SCR unit108.

The SCR unit 108 receives the exhaust from the engine 102, and receivesa reducing agent (also referred to as reductant) from the reductantsystem 106. The SCR unit 108 and reductant unit 106 are configured toreduce the NO_(X) emission of the engine exhaust by using SCR unit 108.

The sensor network 104 may include a first NO_(X) sensor 202 forindicating a NO_(X) level of the exhaust prior to the SCR unit 108; asecond NO_(X) sensor 206 for indicating a NO_(X) level of the exhaustafter the SCR unit 108; a first temperature sensor 204 for indicating atemperature (e.g., a pre-urea injection temperature) of the exhaustprior to the SCR unit 108; a second temperature sensor 208 forindicating a temperature (e.g., a post-urea injection temperature) ofthe exhaust prior to the SCR unit 108, but after the reducing agent hasmixed with the exhaust from the engine 102; and a controller 210configured to electronically communicate with the first NO_(X) sensor202, the second NO_(X) sensor 206, the first temperature sensor 204, andthe second temperature sensor 208, and to determine a quality indicatoraccording to the differential between the first NO_(X) sensor 202 andthe second NO_(X) sensor 206 and between the first temperature sensor204 and the second temperature sensor 208. Sensors 202-208 areelectronically coupled to controller 210 and may be physical or virtualsensors.

The controller 210 is configured to send or receive information to orfrom the sensors (202-208), and may be configured to send or receiveinformation to or from other additional sensors. For instance thecontroller 210 may receive information from physical sensors (e.g.,exhaust and/or reductant flow rate sensors, NO_(X) sensors, enginesensors, ambient condition sensors, etc.), or may generate or utilizepreconfigured virtual sensors (e.g., a virtual NO_(X) sensor, a virtualurea sensor, etc.) at various points in the system.

The controller 210 may be a processing system that monitors and controlsoperation of the machine 100. Controller 210 may be configured tocollect information from various sensors operating within the machine100 and to provide control signals that affect the operations of deviceswithin the machine 100. In one embodiment of the present invention, thecontroller 210 may be part of an engine control module (ECM) thatmonitors and controls the operation of an engine 102 associated withmachine 100. For example, the controller 210 may be a module programmedor hardwired within an ECM that performs functions dedicated to certainembodiments described herein. For example, the controller 210 may beimplemented in software that is stored as instructions and data within amemory device of an ECM and is executed by a processor operating withinthe ECM. Alternatively, the controller 210 may be a module that isseparate from other components of the system, and may be in electroniccommunication with other components of the system.

Controller 210 may include a processor, memory, and an interface. Theprocessor may be a processing device, such as a microcontroller, thatmay exchange data with the memory and interface to perform certainprocesses consistent with features described herein. One skilled in theart would recognize that the controller 210 may include a plurality ofprocessors that may operate collectively to perform functions consistentwith certain embodiments presented herein.

The controller may also be configured to interact with a plurality ofsensors in addition to those shown in FIGS. 1 and 2. These sensors mayinclude a combination of one or more physical and/or virtual sensors.For example, the sensors may include one or more physical sensorsprovided for measuring certain parameters of machine operatingenvironment, such as physical sensors for measuring emissions of machine100, such as Nitrogen Oxides (NO_(X)), Sulfur Dioxide (SO₂), CarbonMonoxide (CO), total reduced Sulfur (TRS), etc. Physical sensors mayinclude any appropriate sensors that are used with engine 102 or othermachine components to provide various measured parameters about engine102 or other components, such as temperature, speed, acceleration rate,fuel pressure, power output, etc.

According to one embodiment, NO_(X) sensor 202 is a physical sensorwhich may be used by the controller 210 to predict a NO_(X) emissionvalue. The sensor 202 may be a single sensor or may reflect acombination of sensors for detecting parameters such as ambienthumidity, manifold pressure, manifold temperature, fuel rate, and enginespeed associated with the engine. Additionally, a first NO_(X) sensor202 may be a physical NO_(X) sensor located upstream of the SCR unit 108or may be a virtual NO_(X) sensor generated by the controller 210 basedon variables such as those provided by the other sensors. A secondNO_(X) sensor 206 may be a physical NO_(X) sensor located downstream ofthe SCR unit 108 or may be a virtual NO_(X) sensor generated by thecontroller 210 based on variables such as those provided by the othersensors.

The controller 210 may register variables such as temperature ortime-of-last-fill of the reductant system 106 to help determine a causeof the deviation from the anticipated NO_(X) values. One or both of thefirst and second NO_(X) sensors 204, 206 may be a virtual sensor.

Furthermore, during a steady state operation of the engine, thetemperature sensor measurement may vary according to the diesel emissionfluid (DEF) injection amount. Therefore, by comparing the differentialbetween the first temperature sensor 204 and the second temperaturesensor 208 to a predicted value, the quality of the DEF fluid and/or thepresence of significant deposits in the system may be determined. Forinstance, a significant deviation from the predicted temperaturedifferential is indicative that a non-standard DEF fluid (e.g., dilutedDEF fluid, diesel, or water, etc.) is being used or that high levels ofurea deposits have formed in the system.

The controller 210 may be further configured to generate a signal whenthe quality index of the reducing agent indicated by the controller 210is not within a tolerance level (e.g., a predefined tolerance level).For example, the controller 210 may be configured to trigger a warninglight or adjust the flow rate of the reductant. For instance, if theNO_(X) reduction is less than expected, the controller 210 may generatea signal to increase the amount of reductant to send to the SCR unit108.

If the temperature drop is low and the NO_(X) reduction is also low, thecontroller 210 may generate a signal indicating that a clogged injectoris likely or that deposits are being formed. And if there is notemperature drop and no NO_(X) reduction then the controller 210 maygenerate a signal indicating that injector failure is likely.

Additionally, if the temperature differential is zero or and/or slightlyincreasing and there is a moderate, but less than anticipated NO_(X)reduction, than the controller 210 may generate a signal indicating thatthe DEF tank may be filled with diesel fluid or another fluid (e.g., anon-urea fluid).

Additionally, the exhaust treatment system 200, the first NO_(X) sensor202 may further indicates a NO_(X) level for the engine exhaust aftertreatment by a filter. The filter may be a diesel particulate filter(DPF).

FIG. 3 is a block diagram of a method of detecting reductant qualityaccording to an embodiment herein. According to FIG. 3, a method fordetecting a reducing agent (e.g., urea or urea mixture) quality 300includes an obtaining a pre-injection (e.g., pre-urea injection)temperature step 302, an obtaining a pre-SCR NO_(X) value step 304, anobtaining a post-injection (e.g., post-urea injection) temperature step306, an obtaining a post-SCR NO_(X) value step 308, a computing a changein temperature step 310, a computing a change in NO_(X) step 312, anevaluating change in temperature and NO_(X) step 314, a computing thereductant quality step 316 (also referred to as a predicting DEF statusstep 316). Optionally, the method 300 may also include a generating awarning step 318. The warning step 318 may further include, but is notlimited to, triggering a warning light or adjusting the flow rate of thereductant. For instance, if the NO_(X) reduction is less than expected,the controller may generate a signal to increase the amount of reductantto send to the SCR unit 108.

During the evaluating change in temperature and NO_(X) step 314, adifferential between the pre-SCR NO_(X) value obtained in step 304 andthe post-SCR NO_(X) obtained in step 308 is compared against a predictedNO_(X) differential. Additionally, during step 314 a differentialbetween the pre-injection temperature value obtained in step 302 and thepost-injection temperature value obtained in step 306 is comparedagainst a predicted temperature differential.

The obtaining a pre-SCR NO_(X) value step 304 includes determining thefirst NO_(X) value according to a first NO_(X) sensor indicating aNO_(X) level for engine exhaust prior to treatment by the SCR unit 108.The obtaining a second NO_(X) value (a post-SCR NO_(X) value) step 308includes determining a value according to a second NO_(X) sensorindicating a NO_(X) level for engine exhaust after treatment by the SCRunit 108.

The computing the reductant quality step 316 includes generating aquality indicator signal, and may also include generating a virtual ureaquality sensor according to the NO_(X) and temperature values.

According to an embodiment herein, the first NO_(X) sensor 202 mayfurther indicate a NO_(X) level for the engine exhaust after treatmentby a filter. Additionally, the method for detecting a reducing agentquality 300 may further include generating a signal when the reductantquality indicator indicated is not within a tolerance range.

The controller 210 may be configured to generate a first signalindicating that the reducing agent low has a low urea concentration whenthe NO_(X) differential is lower than the predicted NO_(X) differentialand the temperature differential is approximate to the predictedtemperature differential; generate a second signal indicating likelyclogged injector when the NO_(X) differential is lower than thepredicted NO_(X) differential and the temperature differential is lowerthan the predicted temperature differential; generate a third signalindicating likely injection failure when the NO_(X) differential isapproximately zero and the temperature differential is approximatelyzero; and generate a fourth signal indicating that the reducing agent islikely diesel when the NO_(X) differential is moderately lower than thepredicted NO_(X) differential and the temperature differential is higherthan the predicted temperature differential (e.g., the increase intemperature between the first and second temperature sensors is greaterthan the expected change in temperature.)

A virtual sensor network (also referred to as a virtual sensor networksystem), as used herein, may refer to a collection of virtual sensorsintegrated and working together using certain control algorithms suchthat the collection of virtual sensors may provide more desired or morereliable sensor output parameters than discrete individual virtualsensors. A virtual sensor network system may include a plurality ofvirtual sensors configured or established according to certain criteriabased on a particular application. Additional sensors may provideinformation about the ambient environmental conditions, such ashumidity, air temperature, and elevation.

A virtual sensor, as used herein, may refer to a mathematical algorithmor model that produces output measures comparable to a physical sensorbased on inputs from other systems. For example, a physical NO_(X)sensor may measure the level of NO_(X) present in the exhaust stream ofthe engine 102 and provide values of the NO_(X) level to othercomponents, such a controller 210; while a virtual NO_(X) sensor mayprovide calculated values of the NO_(X) level to a controller 210 basedon other measured or calculated parameters, such as such as compressionratios, turbocharger efficiency, after cooler characteristics,temperature values, pressure values, ambient conditions, fuel rates, andengine speeds, etc. The term “virtual sensor” may be usedinterchangeably with “virtual sensor model.”

The virtual sensor network system may also facilitate or controloperations of the virtual sensors. The virtual sensors may include anyappropriate virtual sensor providing sensor output parameterscorresponding to one or more physical sensors in machine 100.

Further, the virtual sensor network system may be configured as aseparate control system or, alternatively, may coincide with othercontrol systems such as an ECM. The virtual sensor network system mayalso operate in series with or in parallel to an ECM. Virtual sensornetwork system and/or ECM may be implemented by any appropriate computersystem. Thus, the virtual sensor network system may be implemented onthe controller 210, or e.g., may be implemenedt elsewhere andcommunications therewith may be relayed through the controller 210.Additionally, a computer system may also be configured to design, train,and validate virtual sensors in virtual sensor network and othercomponents of machine 100.

A virtual sensor process model may be established to buildinterrelationships between physical and virtual sensors. After thevirtual sensor process model is established, values of input parametersmay be provided to the virtual sensor process model (e.g., thecontroller 210) to generate values of output parameters based on thegiven values of input parameters and the interrelationships betweeninput parameters and output parameters established by the virtual sensorprocess model.

In certain embodiments, the virtual sensor system may include a NO_(X)virtual sensor to provide levels of NO_(X) emitted from an engine 102,and a virtual reductant sensor to provide a quality level (or qualityindex) of the reductant stored in the reductant system 106 andtransmitted to the SCR unit 108. Input parameters may include anyappropriate type of data associated with NO_(X) levels. For example,input parameters may include parameters that control operations ofvarious response characteristics of engine 102 and/or parameters thatare associated with conditions corresponding to the operations of engine102. For instance, input parameters may include fuel injection timing,compression ratios, turbocharger efficiency, after coolercharacteristics, temperature values (e.g., intake manifold temperature),pressure values (e.g., intake manifold pressure), ambient conditions(e.g., ambient humidity), fuel rates, and engine speeds, etc. Otherparameters, however, may also be included. For example, parametersoriginated from other vehicle systems, such as chosen transmission gear,axle ratio, elevation and/or inclination of the vehicle, etc., may alsobe included. Further, input parameters may be measured by certainphysical sensors, or created by other control systems such as an ECM.

A virtual sensor process model may include any appropriate type ofmathematical or physical model indicating interrelationships betweeninput parameters and output parameters. For example, the virtual sensorprocess model may be a neural network based mathematical model that istrained to capture interrelationships between input parameters andoutput parameters. Other types of mathematic models, such as fuzzy logicmodels, linear system models, and/or non-linear system models, etc., mayalso be used. Virtual sensor process model may be trained and validatedusing data records collected from a particular engine application forwhich virtual sensor process model is established. That is, the virtualsensor process model may be established according to particular rulescorresponding to a particular type of model using the data records, andthe interrelationships of virtual sensor process model may be verifiedby using part of the data records.

After the virtual sensor process model is trained and validated, virtualsensor process model may be optimized to define a desired input space ofinput parameters and/or a desired distribution of output parameters. Thevalidated or optimized virtual sensor process model may be used toproduce corresponding values of output parameters when provided with aset of values of input parameters.

Thus, a controller 210 may be configured to generate or to utilize apreconfigured virtual sensor model to determine predicted NO_(X) valuesbased on a model reflecting a predetermined relationship between controlparameters and NO_(X) emissions, wherein the control parameters includeambient humidity, manifold pressure, manifold temperature, fuel rate,and engine speed associated with the engine. Additional sensors mayprovide information about the ambient environmental conditions, such ashumidity, air temperature, and elevation. Additionally, the virtualsensor network can utilize additional sensors for detecting the flowrate of the exhaust through the SCR and the flow rate of the reductantthrough the SCR.

If the controller 210 (or the ECM or processor operating the virtualnetwork) determines that any individual input parameter or outputparameter is out of the respective range of the input space or outputspace, the controller may send out a notification to other computerprograms, control systems, or a user of machine 100.

Optionally, controller 210 (or the ECM or processor operating thevirtual network) may also apply any appropriate algorithm to maintainthe values of input parameters or output parameters in the valid rangeto maintain operation with a reduced capacity. For instance, reducingthe engine speed to reduce the flow rate of the exhaust, or increase theflow rate of the reductant in order to increase the reduction of NO_(X).

The controller 210 (or the ECM or processor operating the virtualnetwork) may also determine collectively whether the values of inputparameters are within a valid range. For example, a processor may use aMahalanobis distance to determine normal operational condition ofcollections of input values.

During training and optimizing of virtual sensor models, a validMahalanobis distance range for the input space may be calculated andstored as calibration data associated with individual virtual sensormodels. In operation, a processor may calculate a Mahalanobis distancefor input parameters of a particular virtual sensor model as a validitymetric of the valid range of the particular virtual sensor model. If thecalculated Mahalanobis distance exceeds the range of the validMahalanobis distance range stored in the virtual sensor network, thecontroller 210 may send out a notification to other computer programs,control systems, or a user of machine 100 to indicate that theparticular virtual sensor may be unfit to provide predicted values.

Other validity metrics may also be used. For example, a processor mayevaluate each input parameter against an established upper and lowerbounds of acceptable input parameter values and may perform a logicalAND operation on a collection of evaluated input parameters to obtain anoverall validity metric of the virtual sensor model.

After monitoring and controlling individual virtual sensors, thecontroller 210 (e.g., virtual sensor network processor) may also monitorand control collectively a plurality of virtual sensor models. That is,the controller 210 may determine and control operational fitness of thevirtual sensor network. A processor may monitor any operational virtualsensor model. The processor may also determine whether there is anyinterdependency among any operational virtual sensor models includingthe virtual sensor models becoming operational. If the controller 210determines there is interdependency between any virtual sensor models,the controller 210 may determine that the interdependency between thevirtual sensors may have created a closed loop to connect two or morevirtual sensor models together, which may be neither intended nortested.

The controller 210 may then determine that the virtual sensor networkmay be unfit to make predictions, and may send a notification or reportto control systems, such as ECM, or users of the machine 100. That is,the controller (e.g., a processor) may present other control systems orusers with the undesired condition via a sensor output interface.Alternatively, the controller may indicate as unfit only theinterdependent virtual sensors, while keeping the remaining virtualsensors in operation.

As used herein, a decision that a virtual sensor or a virtual sensornetwork is unfit is intended to include any instance in which any inputparameter to or any output parameter from the virtual sensor or thevirtual sensor network is beyond a valid range or is uncertain, or anyoperational condition that affects the predictability and/or stabilityof the virtual sensor or the virtual sensor network. An unfit virtualsensor network may continue to provide sensing data to other controlsystems using virtual sensors not affected by the unfit condition, suchas interdependency, etc.

The controller 210 may also resolve unfit conditions resulting fromunwanted interdependencies between active virtual sensor models bydeactivating one or more models of lower priority than those remainingactive virtual sensor models.

For instance, if a first active virtual sensor model has a high priorityfor operation of machine 100 but has an unresolved interdependency witha second active virtual sensor having a low priority for operation ofmachine 100, the second virtual sensor model may be deactivated topreserve the integrity of the first active virtual sensor model.

INDUSTRIAL APPLICABILITY

The disclosed reductant quality sensing system may be implemented in anexhaust after-treatment system in various machines. A reductant qualitysensing system provides for enhanced reliability of the NO_(X) reductionprocess by verifying the integrity of the reductant and/or an indicationas to the proper operation of the system that adds reductant to theexhaust stream (e.g., a prediction of the status of urea injectors).

Although certain embodiments have been illustrated and described hereinfor purposes of description, it will be appreciated by those of ordinaryskill in the art that a wide variety of alternate and/or equivalentembodiments or implementations calculated to achieve the same purposesmay be substituted for the embodiments shown and described withoutdeparting from the scope of the present disclosure. Those with skill inthe art will readily appreciate that embodiments in accordance with thepresent invention may be implemented in a very wide variety of ways.This application is intended to cover any adaptations or variations ofthe embodiments discussed herein. Therefore, it is intended thatembodiments in accordance with the present invention be limited only bythe claims and the equivalents thereof.

What is claimed is:
 1. An exhaust treatment system for treating a flowof exhaust produced by an engine comprising: a selective catalystreduction (SCR) unit; a reducing agent dispensing unit configured tointroduce a reducing agent into the exhaust; a first NO_(X) sensorconfigured to indicate a NO_(X) emission level of the exhaust at alocation upstream of the SCR unit; a second NO_(X) sensor configured toindicate a NO_(X) emission level of the exhaust at a location downstreamof the SCR unit; a first temperature sensor configured to indicate atemperature of the exhaust at a location upstream of where the reducingagent is introduced into the exhaust; a second temperature sensorconfigured to indicate a temperature of the exhaust at a locationdownstream of where the reducing agent is introduced into the exhaustand upstream of the SCR unit; and a controller configured toelectronically communicate with the first NO_(X) sensor, the secondNO_(X) sensor, the first temperature sensor, and the second temperaturesensor, and to determine a reductant quality indicator according to aNO_(X) differential between the first NO_(X) sensor and the secondNO_(X) sensor relative to a predicted NO_(X) differential and atemperature differential between the first temperature sensor and thesecond temperature sensor relative to a predicted temperaturedifferential.
 2. The exhaust treatment system of claim 1, furthercomprising a filter, and wherein the first NO_(X) sensor is configuredto indicate a NO_(X) level of the exhaust downstream of the filter. 3.The exhaust treatment system of claim 2, wherein the filter is a dieselparticulate filter (DPF).
 4. The exhaust treatment system of claim 2,further comprising generating a signal when the reductant qualityindicator is not within a tolerance level.
 5. The exhaust treatmentsystem of claim 2, wherein the reducing agent is urea.
 6. The exhausttreatment system of claim 5, wherein the controller is configured togenerate a first signal indicating that the reducing agent has a lowurea concentration when the NO_(X) differential is lower than thepredicted NO_(X) differential and the temperature differential isapproximately equal to the predicted temperature differential.
 7. Theexhaust treatment system of claim 5, wherein the controller isconfigured to generate a second signal indicating a likelihood of aclogged injector when the NO_(X) differential is lower than thepredicted NO_(X) differential and the temperature differential is lowerthan the predicted temperature differential.
 8. The exhaust treatmentsystem of claim 5, wherein the controller is configured to generate athird signal indicating a likelihood of an injection failure when theNO_(X) differential is approximately zero and the temperaturedifferential is approximately zero.
 9. The exhaust treatment system ofclaim 5, wherein the controller is configured to generate a fourthsignal indicating that the reducing agent is likely diesel when theNO_(X) differential is moderately lower than the predicted NO_(X)differential and the temperature differential is higher than thepredicted temperature differential.
 10. The exhaust treatment system ofclaim 5, wherein the controller is configured to: generate a firstsignal indicating that the reducing agent has a low urea concentrationwhen the NO_(X) differential is lower than the predicted NO_(X)differential and the temperature differential is approximately equal tothe predicted temperature differential; generate a second signalindicating likely clogged injector when the NO_(X) differential is lowerthan the predicted NO_(X) differential and the temperature differentialis lower than the predicted temperature differential; generate a thirdsignal indicating likely injection failure when the NO_(X) differentialis approximately zero and the temperature differential is approximatelyzero; and generate a fourth signal indicating that the reducing agent islikely diesel when the NO_(X) differential is moderately lower than thepredicted NO_(X) differential and the temperature differential is higherthan the predicted temperature differential.
 11. A method for detectinga reducing agent quality comprising: obtaining a first NO_(X) valueindicating a NO_(X) level for an engine exhaust upstream of a selectivecatalyst reduction (SCR) unit; obtaining a second NO_(X) valueindicating a NO_(X) emission level for the engine exhaust downstream ofthe SCR unit; obtaining a first temperature value indicating atemperature for the engine exhaust upstream of an introduction of areducing agent; obtaining a second temperature value indicating atemperature for the engine exhaust downstream of the introduction of thereducing agent and upstream of the SCR unit; and computing a reductantquality indicator according to a NO_(X) differential between the firstNO_(X) value and the second NO_(X) value relative to a predicted NO_(X)differential and a temperature differential between the firsttemperature value and the second temperature value relative to apredicted temperature differential.
 12. The method of claim 11, whereinthe first NO_(X) value further indicates a NO_(X) level for the engineexhaust after treatment by a filter.
 13. The method of claim 11, furthercomprising generating a signal when the reductant quality indicatorindicated is not within a tolerance level.
 14. The method of claim 11,further comprising adjusting a rate at which the reducing agent isinjected into the exhaust according to the reductant quality indicator.15. The method of claim 11, wherein the reducing agent is urea.
 16. Themethod of claim 15, further comprising generating a first signalindicating that the reducing agent has a low urea concentration when theNO_(X) differential is lower than the predicted NO_(X) differential andthe temperature differential is approximately equal to the predictedtemperature differential.
 17. The method of claim 15, further comprisinggenerating a second signal indicating likely clogged injector when theNO_(X) differential is lower than the predicted NO_(X) differential andthe temperature differential is lower than the predicted temperaturedifferential.
 18. The method of claim 15, further comprising generatinga third signal indicating likely injection failure when the NO_(X)differential is approximately zero and the temperature differential isapproximately zero.
 19. The method of claim 15, further comprisinggenerating a fourth signal indicating that the reducing agent is likelydiesel when the NO_(X) differential is moderately lower than thepredicted NO_(X) differential and the temperature differential is higherthan the predicted temperature differential.
 20. The method of claim 15,further comprising: generating a first signal indicating that thereducing agent has a low urea concentration when the NO_(X) differentialis lower than the predicted NO_(X) differential and the temperaturedifferential is approximately equal to the predicted temperaturedifferential; generating a second signal indicating likely cloggedinjector when the NO_(X) differential is lower than the predicted NO_(X)differential and the temperature differential is lower than thepredicted temperature differential; generating a third signal indicatinglikely injection failure when the NO_(X) differential is approximatelyzero and the temperature differential is approximately zero; andgenerating a fourth signal indicating that the reducing agent is likelydiesel when the NO_(X) differential is moderately lower than thepredicted NO_(X) differential and the temperature differential is higherthan the predicted temperature differential.